The GANformer [1]} is a transformer-based Generative Adversarial Network: it consists of a generator \(G(Z)=X\)  that translates a partitioned latent \({Z}^{k{\times }d} = [z_1,...,z_k]\)  into an image \({X}^{HWc}\) , and a discriminator \(D(X)\)  that seeks to discern between real and fake images. The generator begins by mapping the normally-distributed latents into an intermediate space \(W=[w_1,...,w_k]\)  using a feed-forward network. Then, it sets an initial 4\(\times \) 4 grid, which gradually evolves through convolution and upsampling layers to the final image. Contrary to traditional GANs, the GANformer also incorporates bipartite-transformer layers into the generator, which compute attention between the \(k\)  latents and the image features, to support spatial and contentual region-wise modulation:
\(\text{Attention}(X,W) = \text{softmax}\left(\frac{q(X)k(W)^{T}}{\sqrt{d}}\right) v(W)  \\{u}({X},{W}) = \gamma _a(X,W) \odot {LayerNorm}({X}) + \beta _a(X,W) \qquad \mathrm {(1,2)}\) 
